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Automatic Delineation of the Myocardial Wall from CT Images via Shape Segmentation and Variational Region Growing

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Biomed Eng
Volume Number:
Issue Number:
IEEE Trans Biomed Eng. 2013 Oct;60(10):2887-95.
PubMed ID:
Left ventricle (LV), myocardial wall segmentation, right ventricle (RV), salient component, shape segmentation, variational region growing
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 HL085417/HL/NHLBI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Zhu L., Gao Y., Appia V., Yezzi A., Arepalli C., Faber T., Stillman A., Tannenbaum A. Automatic Delineation of the Myocardial Wall from CT Images via Shape Segmentation and Variational Region Growing. IEEE Trans Biomed Eng. 2013 Oct;60(10):2887-95. PMID: 23744658. PMCID: PMC4000443.
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Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images. In the method, the left and right ventricles are located sequentially, in which each ventricle is detected by first identifying the endocardium and then segmenting the epicardium. To this end, the endocardium is localized by utilizing its geometric features obtained on-line from a CT image. After that, a variational region-growing model is employed to extract the epicardium of the ventricles. In particular, the location of the endocardium of the left ventricle is determined via using an active contour model on the blood-pool surface. To localize the right ventricle, the active contour model is applied on a heart surface extracted based on the left ventricle segmentation result. The robustness and accuracy of the proposed approach is demonstrated by experimental results from 33 human and 12 pig CT images.

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